From 46e929bfce20cb199c652d58cf49b06fe5872754 Mon Sep 17 00:00:00 2001 From: dmy Date: Thu, 8 Jan 2026 09:46:00 +0800 Subject: [PATCH] Implement genetic algorithm for collector layout optimization --- ga.py | 193 ++++++++++++++++++++++++++++++++++++++++++ gui.py | 46 +++++++--- main.py | 257 +++++++++++++++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 474 insertions(+), 22 deletions(-) create mode 100644 ga.py diff --git a/ga.py b/ga.py new file mode 100644 index 0000000..acbd877 --- /dev/null +++ b/ga.py @@ -0,0 +1,193 @@ +import numpy as np +import pandas as pd +from scipy.spatial import distance_matrix +from scipy.sparse.csgraph import minimum_spanning_tree +from collections import defaultdict +import random + + +def design_with_ga( + turbines, + substation, + cable_specs=None, + voltage=66000, + power_factor=0.95, + system_params=None, + pop_size=50, + generations=50, + evaluate_func=None, + total_invest_func=None, + get_max_capacity_func=None, +): + """ + 使用遗传算法优化集电线路布局 + :param turbines: 风机DataFrame + :param substation: 升压站坐标 + :param cable_specs: 电缆规格 + :param system_params: 系统参数（用于NPV计算） + :param pop_size: 种群大小 + :param generations: 迭代代数 + :param evaluate_func: 评估函数 + :param total_invest_func: 总投资计算函数 + :param get_max_capacity_func: 获取最大容量函数 + :return: 连接列表和带有簇信息的turbines + """ + if get_max_capacity_func: + max_mw = get_max_capacity_func(cable_specs, voltage, power_factor) + else: + max_mw = 100.0 # 默认值 + total_power = turbines["power"].sum() + max_clusters = int(np.ceil(total_power / max_mw)) + n_turbines = len(turbines) + + # 预计算距离矩阵 + all_coords = np.vstack([substation, turbines[["x", "y"]].values]) + dist_matrix_full = distance_matrix(all_coords, all_coords) + + def fitness(chromosome): + cluster_assign = chromosome + clusters = defaultdict(list) + for i, c in enumerate(cluster_assign): + clusters[c].append(i) + + connections = [] + for c, members in clusters.items(): + if len(members) == 0: + continue + coords = turbines.iloc[members][["x", "y"]].values + if len(members) > 1: + dm = distance_matrix(coords, coords) + mst = minimum_spanning_tree(dm).toarray() + for i in range(len(members)): + for j in range(len(members)): + if mst[i, j] > 0: + connections.append( + ( + f"turbine_{members[i]}", + f"turbine_{members[j]}", + mst[i, j], + ) + ) + # 连接到升压站 + dists = [dist_matrix_full[0, m + 1] for m in members] + closest = members[np.argmin(dists)] + connections.append((f"turbine_{closest}", "substation", min(dists))) + + eval_res = evaluate_func( + turbines, + connections, + substation, + cable_specs, + is_offshore=False, + method_name="GA", + voltage=voltage, + power_factor=power_factor, + ) + if system_params and total_invest_func: + res_list = total_invest_func( + [ + { + "cost": eval_res["total_cost"], + "loss": eval_res["total_loss"], + "eval": eval_res, + } + ], + system_params, + ) + return res_list[0]["total_cost_npv"] + return eval_res["total_cost"] + + def init_individual(): + assign = np.zeros(n_turbines, dtype=int) + cluster_powers = np.zeros(max_clusters) + for i in range(n_turbines): + p = turbines.iloc[i]["power"] + possible = [ + c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw + ] + if possible: + c = random.choice(possible) + else: + c = random.randint(0, max_clusters - 1) + assign[i] = c + cluster_powers[c] += p + return assign.tolist() + + population = [init_individual() for _ in range(pop_size)] + best = None + best_fitness = float("inf") + + for gen in range(generations): + fitnesses = [fitness(ind) for ind in population] + min_fit = min(fitnesses) + if min_fit < best_fitness: + best_fitness = min_fit + best = population[fitnesses.index(min_fit)].copy() + + def tournament(size=3): + candidates = random.sample(list(zip(population, fitnesses)), size) + return min(candidates, key=lambda x: x[1])[0] + + selected = [tournament() for _ in range(pop_size)] + + new_pop = [] + for i in range(0, pop_size, 2): + p1 = selected[i] + p2 = selected[i + 1] if i + 1 < pop_size else selected[0] + if random.random() < 0.8: + point = random.randint(1, n_turbines - 1) + child1 = p1[:point] + p2[point:] + child2 = p2[:point] + p1[point:] + else: + child1, child2 = p1.copy(), p2.copy() + new_pop.extend([child1, child2]) + + for ind in new_pop: + if random.random() < 0.1: + idx = random.randint(0, n_turbines - 1) + old_c = ind[idx] + new_c = random.randint(0, max_clusters - 1) + ind[idx] = new_c + cluster_powers = defaultdict(float) + for j, c in enumerate(ind): + cluster_powers[c] += turbines.iloc[j]["power"] + if max(cluster_powers.values()) > max_mw: + ind[idx] = max_clusters + max_clusters += 1 + + elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[ + : int(0.1 * pop_size) + ] + new_pop[: len(elites)] = [e[0] for e in elites] + population = new_pop[:pop_size] + + # 解码最佳个体 + cluster_assign = best + clusters = defaultdict(list) + for i, c in enumerate(cluster_assign): + clusters[c].append(i) + + connections = [] + for c, members in clusters.items(): + if len(members) == 0: + continue + coords = turbines.iloc[members][["x", "y"]].values + if len(members) > 1: + dm = distance_matrix(coords, coords) + mst = minimum_spanning_tree(dm).toarray() + for i in range(len(members)): + for j in range(len(members)): + if mst[i, j] > 0: + connections.append( + ( + f"turbine_{members[i]}", + f"turbine_{members[j]}", + mst[i, j], + ) + ) + dists = [dist_matrix_full[0, m + 1] for m in members] + closest = members[np.argmin(dists)] + connections.append((f"turbine_{closest}", "substation", min(dists))) + + turbines["cluster"] = cluster_assign + return connections, turbines diff --git a/gui.py b/gui.py index 33848e6..f84a08a 100644 --- a/gui.py +++ b/gui.py @@ -90,6 +90,7 @@ def index(): "run_btn": None, "current_file_container": None, # 替换 label 为 container "info_container": None, # 新增信息展示容器 + "ga_switch": None, # 遗传算法开关 } def update_info_panel(): @@ -347,7 +348,9 @@ def index(): except Exception as ex: ui.notify(f"上传处理失败: {ex}", type="negative") - async def save_file_with_dialog(filename, callback, file_filter="All files (*.*)", sender=None): + async def save_file_with_dialog( + filename, callback, file_filter="All files (*.*)", sender=None + ): """ 跨平台文件保存助手。 如果是原生模式，弹出系统保存对话框。 @@ -372,7 +375,7 @@ def index(): ps_filter = file_filter.replace("(", "|").replace(")", "") if "|" not in ps_filter: ps_filter += f"|{os.path.splitext(filename)[1] or '*.*'}" - + # 简单清洗 filter 字符串以适应 PowerShell (e.g., "Excel Files *.xlsx" -> "Excel Files|*.xlsx") # 这里做一个简化的映射，确保格式正确 if "Excel" in file_filter: @@ -399,9 +402,9 @@ def index(): # 使用 startupinfo 隐藏控制台窗口 (防止黑框闪烁) startupinfo = subprocess.STARTUPINFO() startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - + print("DEBUG: invoking PowerShell SaveFileDialog...") - + # 使用 run.io_bound 在后台线程执行，避免阻塞主事件循环 # 这样按钮的禁用状态可以立即同步到前端 result = await run.io_bound( @@ -409,7 +412,7 @@ def index(): ["powershell", "-Command", ps_script], capture_output=True, text=True, - startupinfo=startupinfo + startupinfo=startupinfo, ) save_path = result.stdout.strip() if save_path: @@ -420,7 +423,7 @@ def index(): else: print("DEBUG: PowerShell dialog cancelled or empty result.") # 用户取消，直接返回，不回退 - return + return except Exception as e: print(f"PowerShell dialog failed: {e}") @@ -477,7 +480,10 @@ def index(): async def on_click_excel(e): await save_file_with_dialog( - default_excel_name, save_excel, "Excel Files (*.xlsx)", sender=e.sender + default_excel_name, + save_excel, + "Excel Files (*.xlsx)", + sender=e.sender, ) ui.button( @@ -607,7 +613,9 @@ def index(): except: pass - await save_file_with_dialog(default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender) + await save_file_with_dialog( + default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender + ) ui.button("导出全部方案 DXF (ZIP)", on_click=on_click_all_dxf).props( "icon=folder_zip color=secondary" @@ -669,6 +677,9 @@ def index(): refs["log_box"].clear() log_queue = queue.Queue() + # 获取遗传算法开关状态 + use_ga = refs["ga_switch"].value if refs["ga_switch"] else False + class QueueLogger(io.StringIO): def write(self, message): if message and message.strip(): @@ -716,6 +727,7 @@ def index(): n_clusters_override=None, interactive=False, plot_results=False, + use_ga=use_ga, ) # 在后台线程运行计算任务 @@ -782,7 +794,11 @@ def index(): total_length_km = total_length_m / 1000 # 获取回路数 (通过统计从升压站发出的连接) - n_circuits = sum(1 for d in res["eval"]["details"] if d["source"] == "substation" or d["target"] == "substation") + n_circuits = sum( + 1 + for d in res["eval"]["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) row_dict = { "name": name_display, @@ -875,7 +891,10 @@ def index(): raise FileNotFoundError("无法生成模板文件") await save_file_with_dialog( - "coordinates.xlsx", save_template, "Excel Files (*.xlsx)", sender=e.sender + "coordinates.xlsx", + save_template, + "Excel Files (*.xlsx)", + sender=e.sender, ) ui.button("导出 Excel 模板", on_click=export_template).classes( @@ -901,6 +920,8 @@ def index(): # with refs["current_file_container"]: # ui.label("未选择文件").classes("text-xs text-gray-500 italic ml-1") + + # 3. 运行按钮 refs["run_btn"] = ( ui.button( @@ -910,6 +931,11 @@ def index(): .classes("flex-1 py-4") .props("icon=play_arrow color=secondary") ) + # 4. 遗传算法开关 + with ui.column().classes("flex-1 gap-0 justify-center items-center"): + refs["ga_switch"] = ui.switch("启用遗传算法", value=False).props( + "color=orange" + ) with ui.column().classes("w-full gap-4"): # 新增：信息展示卡片 diff --git a/main.py b/main.py index 69403df..e34ffbb 100644 --- a/main.py +++ b/main.py @@ -1,6 +1,7 @@ import argparse import math import os +import random from collections import defaultdict import matplotlib.pyplot as plt @@ -12,6 +13,7 @@ from scipy.spatial import distance_matrix from sklearn.cluster import KMeans from esau_williams import design_with_esau_williams +from ga import design_with_ga # 设置matplotlib支持中文显示 plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "Arial"] @@ -654,9 +656,163 @@ def design_with_rotational_sweep( return final_connections, turbines + # 预计算距离矩阵 + all_coords = np.vstack([substation, turbines[["x", "y"]].values]) + dist_matrix_full = distance_matrix(all_coords, all_coords) + + def fitness(chromosome): + cluster_assign = chromosome + clusters = defaultdict(list) + for i, c in enumerate(cluster_assign): + clusters[c].append(i) + + connections = [] + for c, members in clusters.items(): + if len(members) == 0: + continue + coords = turbines.iloc[members][["x", "y"]].values + if len(members) > 1: + dm = distance_matrix(coords, coords) + mst = minimum_spanning_tree(dm).toarray() + for i in range(len(members)): + for j in range(len(members)): + if mst[i, j] > 0: + connections.append( + ( + f"turbine_{members[i]}", + f"turbine_{members[j]}", + mst[i, j], + ) + ) + # 连接到升压站 + dists = [dist_matrix_full[0, m + 1] for m in members] + closest = members[np.argmin(dists)] + connections.append((f"turbine_{closest}", "substation", min(dists))) + + eval_res = evaluate_design( + turbines, + connections, + substation, + cable_specs, + is_offshore=False, + method_name="GA", + voltage=voltage, + power_factor=power_factor, + ) + if system_params: + res_list = total_investment( + [ + { + "cost": eval_res["total_cost"], + "loss": eval_res["total_loss"], + "eval": eval_res, + } + ], + system_params, + ) + return res_list[0]["total_cost_npv"] + return eval_res["total_cost"] + + def init_individual(): + assign = np.zeros(n_turbines, dtype=int) + cluster_powers = np.zeros(max_clusters) + for i in range(n_turbines): + p = turbines.iloc[i]["power"] + possible = [ + c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw + ] + if possible: + c = random.choice(possible) + else: + c = random.randint(0, max_clusters - 1) + assign[i] = c + cluster_powers[c] += p + return assign.tolist() + + population = [init_individual() for _ in range(pop_size)] + best = None + best_fitness = float("inf") + + for gen in range(generations): + fitnesses = [fitness(ind) for ind in population] + min_fit = min(fitnesses) + if min_fit < best_fitness: + best_fitness = min_fit + best = population[fitnesses.index(min_fit)].copy() + + def tournament(size=3): + candidates = random.sample(list(zip(population, fitnesses)), size) + return min(candidates, key=lambda x: x[1])[0] + + selected = [tournament() for _ in range(pop_size)] + + new_pop = [] + for i in range(0, pop_size, 2): + p1 = selected[i] + p2 = selected[i + 1] if i + 1 < pop_size else selected[0] + if random.random() < 0.8: + point = random.randint(1, n_turbines - 1) + child1 = p1[:point] + p2[point:] + child2 = p2[:point] + p1[point:] + else: + child1, child2 = p1.copy(), p2.copy() + new_pop.extend([child1, child2]) + + for ind in new_pop: + if random.random() < 0.1: + idx = random.randint(0, n_turbines - 1) + old_c = ind[idx] + new_c = random.randint(0, max_clusters - 1) + ind[idx] = new_c + cluster_powers = defaultdict(float) + for j, c in enumerate(ind): + cluster_powers[c] += turbines.iloc[j]["power"] + if max(cluster_powers.values()) > max_mw: + ind[idx] = max_clusters + max_clusters += 1 + + elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[ + : int(0.1 * pop_size) + ] + new_pop[: len(elites)] = [e[0] for e in elites] + population = new_pop[:pop_size] + + # 解码最佳个体 + cluster_assign = best + clusters = defaultdict(list) + for i, c in enumerate(cluster_assign): + clusters[c].append(i) + + connections = [] + for c, members in clusters.items(): + if len(members) == 0: + continue + coords = turbines.iloc[members][["x", "y"]].values + if len(members) > 1: + dm = distance_matrix(coords, coords) + mst = minimum_spanning_tree(dm).toarray() + for i in range(len(members)): + for j in range(len(members)): + if mst[i, j] > 0: + connections.append( + ( + f"turbine_{members[i]}", + f"turbine_{members[j]}", + mst[i, j], + ) + ) + dists = [dist_matrix_full[0, m + 1] for m in members] + closest = members[np.argmin(dists)] + connections.append((f"turbine_{closest}", "substation", min(dists))) + + turbines["cluster"] = cluster_assign + return connections, turbines + # 4. 获取电缆最大容量(MW) -def get_max_cable_capacity_mw(cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR): +def get_max_cable_capacity_mw( + cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR +): """ 根据电缆规格计算最大承载功率 :param cable_specs: 电缆规格列表 list of tuples，或者直接是最大功率数值(MW) @@ -991,7 +1147,11 @@ def export_to_excel(connections_details, filename): df = pd.DataFrame(data) # 汇总统计 - n_circuits = sum(1 for conn in connections_details if conn["source"] == "substation" or conn["target"] == "substation") + n_circuits = sum( + 1 + for conn in connections_details + if conn["source"] == "substation" or conn["target"] == "substation" + ) summary = { "Total Cost (¥)": df["Cost (¥)"].sum(), "Total Effective Length (m)": df["Effective Length (m)"].sum(), @@ -1023,7 +1183,11 @@ def export_all_scenarios_to_excel(results, filename): summary_data = [] for res in results: # 获取回路数 (通过统计从升压站发出的连接) - n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation") + n_circuits = sum( + 1 + for conn in res["eval"]["details"] + if conn["source"] == "substation" or conn["target"] == "substation" + ) summary_data.append( { @@ -1230,7 +1394,11 @@ def visualize_design( # 7. 主函数：比较两种设计方法 def compare_design_methods( - excel_path=None, n_clusters_override=None, interactive=True, plot_results=True + excel_path=None, + n_clusters_override=None, + interactive=True, + plot_results=True, + use_ga=False, ): """ 比较MST和三种电缆方案下的K-means设计方法 @@ -1411,7 +1579,11 @@ def compare_design_methods( voltage=voltage, power_factor=power_factor, ) - n_circuits_base = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation") + n_circuits_base = sum( + 1 + for d in eval_base["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) comparison_results.append( { "name": base_name, @@ -1441,7 +1613,11 @@ def compare_design_methods( voltage=voltage, power_factor=power_factor, ) - n_circuits_rot = sum(1 for d in eval_rot["details"] if d["source"] == "substation" or d["target"] == "substation") + n_circuits_rot = sum( + 1 + for d in eval_rot["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) comparison_results.append( { "name": rot_name, @@ -1471,7 +1647,11 @@ def compare_design_methods( voltage=voltage, power_factor=power_factor, ) - n_circuits_ew = sum(1 for d in eval_ew["details"] if d["source"] == "substation" or d["target"] == "substation") + n_circuits_ew = sum( + 1 + for d in eval_ew["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) comparison_results.append( { "name": ew_name, @@ -1486,6 +1666,49 @@ def compare_design_methods( f" [Esau-Williams] Cost: ¥{eval_ew['total_cost']:,.2f} | Loss: {eval_ew['total_loss']:.2f} kW | Circuits: {n_circuits_ew}" ) + if use_ga: + # --- Run 4: Genetic Algorithm --- + ga_name = f"{name} (GA)" + conns_ga, turbines_ga = design_with_ga( + turbines.copy(), + substation, + current_specs, + voltage, + power_factor, + system_params, + evaluate_func=evaluate_design, + total_invest_func=total_investment, + get_max_capacity_func=get_max_cable_capacity_mw, + ) + eval_ga = evaluate_design( + turbines, + conns_ga, + substation, + cable_specs=current_specs, + is_offshore=is_offshore, + method_name=ga_name, + voltage=voltage, + power_factor=power_factor, + ) + n_circuits_ga = sum( + 1 + for d in eval_ga["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) + comparison_results.append( + { + "name": ga_name, + "cost": eval_ga["total_cost"], + "loss": eval_ga["total_loss"], + "eval": eval_ga, + "turbines": turbines_ga, + "specs": current_specs, + } + ) + print( + f" [GA] Cost: ¥{eval_ga['total_cost']:,.2f} | Loss: {eval_ga['total_loss']:.2f} kW | Circuits: {n_circuits_ga}" + ) + # 记录最佳 if eval_rot["total_cost"] < best_cost: best_cost = eval_rot["total_cost"] @@ -1500,7 +1723,11 @@ def compare_design_methods( # 可视化 (只画 Base 版本) ax_idx = i + 1 if plot_results and ax_idx < 4: - n_circuits = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation") + n_circuits = sum( + 1 + for d in eval_base["details"] + if d["source"] == "substation" or d["target"] == "substation" + ) title = f"{base_name} ({n_circuits} circuits)\nCost: ¥{eval_base['total_cost'] / 10000:.2f}万" visualize_design( turbines_base, substation, eval_base["details"], title, ax=axes[ax_idx] @@ -1540,11 +1767,17 @@ def compare_design_methods( for i, res in enumerate(comparison_results): if res["cost"] < comparison_results[best_idx]["cost"]: best_idx = i - + # 获取回路数 (通过统计从升压站发出的连接) - n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation") - - print(f" {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}") + n_circuits = sum( + 1 + for conn in res["eval"]["details"] + if conn["source"] == "substation" or conn["target"] == "substation" + ) + + print( + f" {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}" + ) print(f"推荐方案: {comparison_results[best_idx]['name']} (默认)")